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salmon1802/
QIN

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Official implementation code of DISCO.AHU team

🔥 Winning 2nd Place in WWW2025 Multimodal CTR Prediction Challenge Track

This repository contains the model as presented in Quadratic Interest Network for Multimodal Click-Through Rate Prediction.

🔥 Follow to perfectly reproduce the results of this code.

To facilitate reproducibility, we share the model code on GitHub: https://github.com/salmon1802/QIN
In ./checkpoints and ./submission folders have our run logs and submission files, respectively.
This submission can be reproduced manually by following the actions below, or by directly using the one-click run script run.sh

Data Preparation

Download the datasets at: https://recsys.westlake.edu.cn/MicroLens_1M_MMCTR
Unzip the data files to the data directory

bash
1cd ./data/ 2wget -r -np -nH --cut-dirs=1 http://recsys.westlake.edu.cn/MicroLens_1M_MMCTR/MicroLens_1M_x1/

Environment

We run the experiments on RTX 4090 GPU of AutoDL.com

Please set up the environment as follows.

torch==2.0.0+cu118
fuxictr==2.3.7

conda create -n fuxictr_www python==3.8
pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
source activate fuxictr_www

How to Run

Train the model on train and validation sets:

```
python run_expid.py
```

The parameters QIN_variety_v9 in ./config/qin_config/model_config.yaml are set to the optimal hyperparameters in the environment described above.

Tips

It is worth mentioning that after our tests, we find that although the parameter num_row = 4 achieves the best performance in the above environments, there is training instability in some environments.

When this happens, we suggest that sacrificing some performance in favor of setting num_row = 3 reproduces the results well.

salmon1802/QIN